Spatiotemporal adaptive hybrid dynamic graph convolutional network for traffic flow prediction

Traffic flow prediction is a crucial research area that has been extensively studied using graph-based prediction methods. However, existing approaches often rely on static or dynamic graphs to model spatial dependencies, which may not capture diverse spatial correlations and dependencies arising from intricate traffic patterns. In this paper, we propose a novel Spatiotemporal Adaptive Hybrid Dynamic Graph Convolutional Network (STAHDGCN) to enhance traffic prediction accuracy. Specifically, we propose a hybrid spatial graph learning module designed to capture diverse spatial stability and contingency in the road network at different times. This module incorporates both a static adaptive learning module and a dynamic learning module. Following this, a spatial gate fusion module is employed to conduct feature fusion, effectively simulating the complex spatiotemporal dependence within road networks. Finally, a proposed adaptive spatiotemporal module utilizes an attention mechanism to effectively capture potential dependence patterns in both time and space, addressing the impact of spatial heterogeneity. Experimental evaluations on two public datasets, METRLA and PEMS-BAY, demonstrate the superior performance of our model.